TY - GEN
T1 - Image Retargeting Techniques Identification Using Supervised Deep Learning
AU - Kharsa, Ruba
AU - Alzoubi, Rouaa
AU - Alsmirat, Mohammad
AU - Al-Ayyoub, Mahmoud
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques.
AB - Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques.
KW - auto image re-dimensioning
KW - deep learning
KW - image resolution
KW - image retargeting
UR - https://www.scopus.com/pages/publications/85179765495
U2 - 10.1109/IDSTA58916.2023.10317851
DO - 10.1109/IDSTA58916.2023.10317851
M3 - Conference contribution
AN - SCOPUS:85179765495
T3 - 2023 International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023
SP - 15
EP - 20
BT - 2023 International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023
A2 - Alsmirat, Mohammad
A2 - Jararweh, Yaser
A2 - Aloqaily, Moayad
A2 - Lloret, Jaime
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023
Y2 - 24 October 2023 through 26 October 2023
ER -